#install.packages('TDAmapper')
library(TDAmapper)
library(cluster)
library(vip)
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## Attaching package: 'vip'
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## 
##     vi
#install.packages('kernlab’)
library(kernlab)
#install.packages(‘class’)
library(class)
#install.packages('nnet')
library(nnet)
#install.packages(‘randomForest’)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
#install.packages('e1071')
library(e1071)                                                  
#install.packages("BayesFactor")
library(BayesFactor)
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## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
## 
## Type BFManual() to open the manual.
## ************
library(BayesPPD)
library(bayestestR)
#install.packages('igraph')
library('igraph')
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library(ggplot2)
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#install.packages('networkD3')
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library(rstanarm)
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## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
##   options(mc.cores = parallel::detectCores())
library(see)
#install.packages('tidyverse')
library(tidyverse)
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## ## Copyright (C) 2003-2025 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
## ##
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## ## (Grants SES-0350646 and SES-0350613)
## ##
#linstall.packages("caret")
library(caret)
library(TDA)
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library(stringr)
#install.packages('ks')
library(ks)
library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
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##Add Bayesian tests functions

#create function to conduct the Bayesian Sign Test
BayesianSignTest <- function(diffVector,rope_min,rope_max) {

  library(MCMCpack)

  samples <- 3000

  #build the vector 0.5 1 1 ....... 1 

  weights <- c(0.5,rep(1,length(diffVector)))

  #add the fake first observation in 0

  diffVector <- c (0, diffVector)  


  #for the moment we implement the sign test. Signedrank will follows

  probLeft <- mean (diffVector < rope_min)

  probRope <- mean (diffVector > rope_min & diffVector < rope_max)

  probRight <- mean (diffVector > rope_max)

  results = list ("probLeft"=probLeft, "probRope"=probRope,
                  
                  "probRight"=probRight)
  
  return (results)
}


##Create function to conduct Bayesian Signed Rank Test

BayesianSignedRank <- function(diffVector,rope_min,rope_max) {
  
  library(MCMCpack)
  
  samples <- 30000
  
  #build the vector 0.5 1 1 ....... 1
  weights <- c(0.5,rep(1,length(diffVector)))
  
  #add the fake first observation in 0
  diffVector <- c (0, diffVector)
  
  sampledWeights <- rdirichlet(samples,weights)
  
  winLeft <- vector(length = samples)
  winRope <- vector(length = samples)
  winRight <- vector(length = samples)
  
  for (rep in 1:samples){
    currentWeights <- sampledWeights[rep,]
    for (i in 1:length(currentWeights)){
      for (j in 1:length(currentWeights)){
        product= currentWeights[i] * currentWeights[j]
        if (diffVector[i]+diffVector[j] > (2*rope_max) ) {
          winRight[rep] <- winRight[rep] + product
        }
        else if (diffVector[i]+diffVector[j] > (2*rope_min) ) {
          winRope[rep] <- winRope[rep] + product
        }
        else {
          winLeft[rep] <- winLeft[rep] + product
        }

      }
    }
    maxWins=max(winRight[rep],winRope[rep],winLeft[rep])
    winners = (winRight[rep]==maxWins)*1 + (winRope[rep]==maxWins)*1 + (winLeft[rep]==maxWins)*1
    winRight[rep] <- (winRight[rep]==maxWins)*1/winners
    winRope[rep] <- (winRope[rep]==maxWins)*1/winners
    winLeft[rep] <- (winLeft[rep]==maxWins)*1/winners
  }
  
  
  results = list ("winLeft"=mean(winLeft), "winRope"=mean(winRope),
                  "winRight"=mean(winRight) )
  return (results)
  
}


#Create function to conduct the Bayesian Correlated t.test

#diff_a_b is a vector of differences between the two classifiers, on each fold of cross-validation.
#If you have done 10 runs of 10-folds cross-validation, you have 100 results for each classifier.
#You should have run cross-validation on the same folds for the two classifiers.
#Then diff_a_b is the difference fold-by-fold.

#rho is the correlation of the cross-validation results: 1/(number of folds)
#rope_min and rope_max are the lower and the upper bound of the rope
 
correlatedBayesianTtest <- function(diff_a_b,rho,rope_min,rope_max){
   if (rope_max < rope_min){
     stop("rope_max should be larger than rope_min")
   }
     
  delta <- mean(diff_a_b)
  n <- length(diff_a_b)
  df <- n-1
  stdX <- sd(diff_a_b)
  sp <- sd(diff_a_b)*sqrt(1/n + rho/(1-rho))
  p.left <- pt((rope_min - delta)/sp, df)
  p.rope <- pt((rope_max - delta)/sp, df)-p.left
  results <- list('left'=p.left,'rope'=p.rope,'right'=1-p.left-p.rope)
  return (results)
}
set.seed(16974)

###################################################5.50.5 ROPE Comparisons for Dissertation

##Random Forest Results

rf_dataset_av<-c(0.8603, 0.9179, 0.9829)

rf_pca.5.50.5_n1_av<-c(0.2447, 0.6625, 0.9721)
rf_pca.5.50.5_n2_av<-c(0.4421, 0.8968, 0.9350)
rf_pca.5.50.5_n3_av<-c(0.6138, 0.5603, 0.2911)
rf_pca.5.50.5_n4_av<-c(0.8232, 0.3583, 0.0974)
rf_pca.5.50.5_n5_av<-c(0.7808, 0.1581, 0.0905)

rf_kde.5.50.5_n1_av<-c(0.9157, 0.9164, 0.9935)
rf_kde.5.50.5_n2_av<-c(0.9181, 0.8968, 0.9221)
rf_kde.5.50.5_n3_av<-c(0.9080, 0.7784, 0.9762)
rf_kde.5.50.5_n4_av<-c(0.8903, 0.4966, 0.9076)
rf_kde.5.50.5_n5_av<-c(0.8479, 0.7290, 0.9715)

   
########################   ROPE PCA

diff_rf_pca.5.50.5_n1_av<-rf_dataset_av - rf_pca.5.50.5_n1_av

bsr_diff_rf_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.03726667
## 
## $winRight
## [1] 0.9627333
bsr_diff_rf_pca.5.50.5_n1_av_odds.left<-bsr_diff_rf_pca.5.50.5_n1_av$winLeft/bsr_diff_rf_pca.5.50.5_n1_av$winRight
bsr_diff_rf_pca.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n2_av<-rf_dataset_av - rf_pca.5.50.5_n2_av

bsr_diff_rf_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0087
## 
## $winRight
## [1] 0.9913
bsr_diff_rf_pca.5.50.5_n2_av_odds.left<-bsr_diff_rf_pca.5.50.5_n2_av$winLeft/bsr_diff_rf_pca.5.50.5_n2_av$winRight
bsr_diff_rf_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n3_av<-rf_dataset_av - rf_pca.5.50.5_n3_av

bsr_diff_rf_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008533333
## 
## $winRight
## [1] 0.9914667
bsr_diff_rf_pca.5.50.5_n3_av_odds.left<-bsr_diff_rf_pca.5.50.5_n3_av$winLeft/bsr_diff_rf_pca.5.50.5_n3_av$winRight
bsr_diff_rf_pca.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n4_av<-rf_dataset_av - rf_pca.5.50.5_n4_av

bsr_diff_rf_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0097
## 
## $winRight
## [1] 0.9903
bsr_diff_rf_pca.5.50.5_n4_av_odds.left<-bsr_diff_rf_pca.5.50.5_n4_av$winLeft/bsr_diff_rf_pca.5.50.5_n4_av$winRight
bsr_diff_rf_pca.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n5_av<-rf_dataset_av - rf_pca.5.50.5_n5_av

bsr_diff_rf_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009333333
## 
## $winRight
## [1] 0.9906667
bsr_diff_rf_pca.5.50.5_n5_av_odds.left<-bsr_diff_rf_pca.5.50.5_n5_av$winLeft/bsr_diff_rf_pca.5.50.5_n5_av$winRight
bsr_diff_rf_pca.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_rf_kde.5.50.5_n1_av<-rf_dataset_av - rf_kde.5.50.5_n1_av

bsr_diff_rf_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n1_av
## $winLeft
## [1] 0.6070667
## 
## $winRope
## [1] 0.3929333
## 
## $winRight
## [1] 0
bsr_diff_rf_kde.5.50.5_n1_av_odds.left<-bsr_diff_rf_kde.5.50.5_n1_av$winLeft/bsr_diff_rf_kde.5.50.5_n1_av$winRight
bsr_diff_rf_kde.5.50.5_n1_av_odds.left
## [1] Inf
plot(rope(diff_rf_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n2_av<-rf_dataset_av - rf_kde.5.50.5_n2_av

bsr_diff_rf_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n2_av
## $winLeft
## [1] 0.3281667
## 
## $winRope
## [1] 0.04863333
## 
## $winRight
## [1] 0.6232
bsr_diff_rf_kde.5.50.5_n2_av_odds.left<-bsr_diff_rf_kde.5.50.5_n2_av$winLeft/bsr_diff_rf_kde.5.50.5_n2_av$winRight
bsr_diff_rf_kde.5.50.5_n2_av_odds.left
## [1] 0.5265832
plot(rope(diff_rf_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n3_av<-rf_dataset_av - rf_kde.5.50.5_n3_av

bsr_diff_rf_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n3_av
## $winLeft
## [1] 0.3063333
## 
## $winRope
## [1] 0.1985333
## 
## $winRight
## [1] 0.4951333
bsr_diff_rf_kde.5.50.5_n3_av_odds.left<-bsr_diff_rf_kde.5.50.5_n3_av$winLeft/bsr_diff_rf_kde.5.50.5_n3_av$winRight
bsr_diff_rf_kde.5.50.5_n3_av_odds.left
## [1] 0.6186886
plot(rope(diff_rf_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n4_av<-rf_dataset_av - rf_kde.5.50.5_n4_av

bsr_diff_rf_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n4_av
## $winLeft
## [1] 0.1048333
## 
## $winRope
## [1] 0.01556667
## 
## $winRight
## [1] 0.8796
bsr_diff_rf_kde.5.50.5_n4_av_odds.left<-bsr_diff_rf_kde.5.50.5_n4_av$winLeft/bsr_diff_rf_kde.5.50.5_n4_av$winRight
bsr_diff_rf_kde.5.50.5_n4_av_odds.left
## [1] 0.119183
plot(rope(diff_rf_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n5_av<-rf_dataset_av - rf_kde.5.50.5_n5_av

bsr_diff_rf_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.09226667
## 
## $winRight
## [1] 0.9077333
bsr_diff_rf_kde.5.50.5_n5_av_odds.left<-bsr_diff_rf_kde.5.50.5_n5_av $winLeft/bsr_diff_rf_kde.5.50.5_n5_av$winRight
bsr_diff_rf_kde.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_rf_kde.5.50.5_n5_av,c(-0.01,0.01)))

################################  Support Vector Machine

##Support Vector Machine Results

svm_dataset_av<-c(0.8259, 0.9270, 0.9825)

svm_pca.5.50.5_n1_av<-c(0.3525, 0.5674, 0.9104)
svm_pca.5.50.5_n2_av<-c(0.3546, 0.8868, 0.9218)
svm_pca.5.50.5_n3_av<-c(0.7441, 0.4949, 0.3014)
svm_pca.5.50.5_n4_av<-c(0.7728, 0.3495, 0.0900)
svm_pca.5.50.5_n5_av<-c(0.7592, 0.0382, 0.0896)

svm_kde.5.50.5_n1_av<-c(0.8147, 0.955, 0.9104)
svm_kde.5.50.5_n2_av<-c(0.8076, 0.947, 0.9132)
svm_kde.5.50.5_n3_av<-c(0.8026, 0.918, 0.9104)
svm_kde.5.50.5_n4_av<-c(0.8388, 0.820, 0.9119)
svm_kde.5.50.5_n5_av<-c(0.8051, 0.754, 0.9104)

   
########################   ROPE PCA

diff_svm_pca.5.50.5_n1_av<-svm_dataset_av - svm_pca.5.50.5_n1_av

bsr_diff_svm_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008233333
## 
## $winRight
## [1] 0.9917667
bsr_diff_svm_pca.5.50.5_n1_av_odds.left<-bsr_diff_svm_pca.5.50.5_n1_av$winLeft/bsr_diff_svm_pca.5.50.5_n1_av$winRight
bsr_diff_svm_pca.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n2_av<-svm_dataset_av - svm_pca.5.50.5_n2_av

bsr_diff_svm_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008933333
## 
## $winRight
## [1] 0.9910667
bsr_diff_svm_pca.5.50.5_n2_av_odds.left<-bsr_diff_svm_pca.5.50.5_n2_av$winLeft/bsr_diff_svm_pca.5.50.5_n1_av$winRight
bsr_diff_svm_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n3_av<-svm_dataset_av - svm_pca.5.50.5_n3_av

bsr_diff_svm_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0093
## 
## $winRight
## [1] 0.9907
bsr_diff_svm_pca.5.50.5_n3_av_odds.left<-bsr_diff_svm_pca.5.50.5_n3_av$winLeft/bsr_diff_svm_pca.5.50.5_n3_av$winRight
bsr_diff_svm_pca.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n4_av<-svm_dataset_av - svm_pca.5.50.5_n4_av

bsr_diff_svm_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009066667
## 
## $winRight
## [1] 0.9909333
bsr_diff_svm_pca.5.50.5_n4_av_odds.left<-bsr_diff_svm_pca.5.50.5_n4_av$winLeft/bsr_diff_svm_pca.5.50.5_n4_av$winRight
bsr_diff_svm_pca.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n5_av<-svm_dataset_av - svm_pca.5.50.5_n5_av

bsr_diff_svm_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0081
## 
## $winRight
## [1] 0.9919
bsr_diff_svm_pca.5.50.5_n5_av_odds.left<-bsr_diff_svm_pca.5.50.5_n5_av$winLeft/bsr_diff_svm_pca.5.50.5_n5_av$winRight
bsr_diff_svm_pca.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_svm_kde.5.50.5_n1_av<-svm_dataset_av - svm_kde.5.50.5_n1_av

bsr_diff_svm_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n1_av
## $winLeft
## [1] 0.1505333
## 
## $winRope
## [1] 0.1516667
## 
## $winRight
## [1] 0.6978
bsr_diff_svm_kde.5.50.5_n1_av_odds.left<-bsr_diff_svm_kde.5.50.5_n1_av$winLeft/bsr_diff_svm_kde.5.50.5_n1_av$winRight
bsr_diff_svm_kde.5.50.5_n1_av_odds.left
## [1] 0.2157256
plot(rope(diff_svm_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n2_av<-svm_dataset_av - svm_kde.5.50.5_n2_av

bsr_diff_svm_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n2_av
## $winLeft
## [1] 0.07816667
## 
## $winRope
## [1] 0.2413667
## 
## $winRight
## [1] 0.6804667
bsr_diff_svm_kde.5.50.5_n2_av_odds.left<-bsr_diff_svm_kde.5.50.5_n2_av$winLeft/bsr_diff_svm_kde.5.50.5_n2_av$winRight
bsr_diff_svm_kde.5.50.5_n2_av_odds.left
## [1] 0.1148721
plot(rope(diff_svm_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n3_av<-svm_dataset_av - svm_kde.5.50.5_n3_av

bsr_diff_svm_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1435
## 
## $winRight
## [1] 0.8565
bsr_diff_svm_kde.5.50.5_n3_av_odds.left<-bsr_diff_svm_kde.5.50.5_n3_av$winLeft/bsr_diff_svm_kde.5.50.5_n3_av$winRight
bsr_diff_svm_kde.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_svm_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n4_av<-svm_dataset_av - svm_kde.5.50.5_n4_av

bsr_diff_svm_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n4_av
## $winLeft
## [1] 0.0611
## 
## $winRope
## [1] 0.05026667
## 
## $winRight
## [1] 0.8886333
bsr_diff_svm_kde.5.50.5_n4_av_odds.left<-bsr_diff_svm_kde.5.50.5_n4_av$winLeft/bsr_diff_svm_kde.5.50.5_n4_av$winRight
bsr_diff_svm_kde.5.50.5_n4_av_odds.left
## [1] 0.06875727
plot(rope(diff_svm_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n5_av<-svm_dataset_av - svm_kde.5.50.5_n5_av

bsr_diff_svm_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009166667
## 
## $winRight
## [1] 0.9908333
bsr_diff_svm_kde.5.50.5_n5_av_odds.left<-bsr_diff_svm_kde.5.50.5_n5_av$winLeft/bsr_diff_svm_kde.5.50.5_n5_av$winRight
bsr_diff_svm_kde.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_svm_kde.5.50.5_n5_av,c(-0.01,0.01)))

#########################  Neural Network

##Neural Network Results

nn1_dataset_av<-c(0.8112, 0.6098, 0.9829)

nn1_pca.5.50.5_n1_av<-c(0.2408, 0.3762, 0.9104)
nn1_pca.5.50.5_n2_av<-c(0.7940, 0.1936, 0.9262)
nn1_pca.5.50.5_n3_av<-c(0.7921, 0.2667, 0.8648)
nn1_pca.5.50.5_n4_av<-c(0.7908, 0.2806, 0.0896)
nn1_pca.5.50.5_n5_av<-c(0.7592, 0.0382, 0.0896)

nn1_kde.5.50.5_n1_av<-c(0.7874, 0.1417, 0.9836)
nn1_kde.5.50.5_n2_av<-c(0.7970, 0.5127, 0.9814)
nn1_kde.5.50.5_n3_av<-c(0.8021, 0.5179, 0.9814)
nn1_kde.5.50.5_n4_av<-c(0.7879, 0.2605, 0.9104)
nn1_kde.5.50.5_n5_av<-c(0.7971, 0.3983, 0.9600)

   
########################   ROPE PCA

diff_nn1_pca.5.50.5_n1_av<-nn1_dataset_av - nn1_pca.5.50.5_n1_av

bsr_diff_nn1_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008833333
## 
## $winRight
## [1] 0.9911667
bsr_diff_nn1_pca.5.50.5_n1_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n1_av$winLeft/bsr_diff_nn1_pca.5.50.5_n1_av$winRight
bsr_diff_nn1_pca.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n2_av<-nn1_dataset_av - nn1_pca.5.50.5_n2_av

bsr_diff_nn1_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.03836667
## 
## $winRight
## [1] 0.9616333
bsr_diff_nn1_pca.5.50.5_n2_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n2_av$winLeft/bsr_diff_nn1_pca.5.50.5_n2_av$winRight
bsr_diff_nn1_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n3_av<-nn1_dataset_av - nn1_pca.5.50.5_n3_av

bsr_diff_nn1_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0356
## 
## $winRight
## [1] 0.9644
bsr_diff_nn1_pca.5.50.5_n3_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n3_av$winLeft/bsr_diff_nn1_pca.5.50.5_n3_av$winRight
bsr_diff_nn1_pca.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n4_av<-nn1_dataset_av - nn1_pca.5.50.5_n4_av

bsr_diff_nn1_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008566667
## 
## $winRight
## [1] 0.9914333
bsr_diff_nn1_pca.5.50.5_n4_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n4_av$winLeft/bsr_diff_nn1_pca.5.50.5_n4_av$winRight
bsr_diff_nn1_pca.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n5_av<-nn1_dataset_av - nn1_pca.5.50.5_n5_av

bsr_diff_nn1_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008466667
## 
## $winRight
## [1] 0.9915333
bsr_diff_nn1_pca.5.50.5_n5_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n5_av$winLeft/bsr_diff_nn1_pca.5.50.5_n5_av$winRight
bsr_diff_nn1_pca.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_nn1_kde.5.50.5_n1_av<-nn1_dataset_av - nn1_kde.5.50.5_n1_av

bsr_diff_nn1_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1451667
## 
## $winRight
## [1] 0.8548333
bsr_diff_nn1_kde.5.50.5_n1_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n1_av$winLeft/bsr_diff_nn1_kde.5.50.5_n1_av$winRight
bsr_diff_nn1_kde.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n2_av<-nn1_dataset_av - nn1_kde.5.50.5_n2_av

bsr_diff_nn1_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.3944333
## 
## $winRight
## [1] 0.6055667
bsr_diff_nn1_kde.5.50.5_n2_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n2_av$winLeft/bsr_diff_nn1_kde.5.50.5_n2_av$winRight
bsr_diff_nn1_kde.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n3_av<-nn1_dataset_av - nn1_kde.5.50.5_n3_av

bsr_diff_nn1_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.5775
## 
## $winRight
## [1] 0.4225
bsr_diff_nn1_kde.5.50.5_n3_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n3_av$winLeft/bsr_diff_nn1_kde.5.50.5_n3_av$winRight
bsr_diff_nn1_kde.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n4_av<-nn1_dataset_av - nn1_kde.5.50.5_n4_av

bsr_diff_nn1_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008133333
## 
## $winRight
## [1] 0.9918667
bsr_diff_nn1_kde.5.50.5_n4_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n4_av$winLeft/bsr_diff_nn1_kde.5.50.5_n4_av$winRight
bsr_diff_nn1_kde.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n5_av<-nn1_dataset_av - nn1_kde.5.50.5_n5_av

bsr_diff_nn1_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.03726667
## 
## $winRight
## [1] 0.9627333
bsr_diff_nn1_kde.5.50.5_n5_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n5_av$winLeft/bsr_diff_nn1_kde.5.50.5_n5_av$winRight
bsr_diff_nn1_kde.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.50.5_n5_av,c(-0.01,0.01)))

################################  Logistic Regression

##Logistic Regression Results

lr_dataset_av<-c(0.8570, 0.9201, 0.9829)

lr_pca.5.50.5_n1_av<-c(0.2445, 0.5934, 0.9829)
lr_pca.5.50.5_n2_av<-c(0.3665, 0.9228, 0.9620)
lr_pca.5.50.5_n3_av<-c(0.5870, 0.6684, 0.8968)
lr_pca.5.50.5_n4_av<-c(0.7250, 0.3689, 0.5709)
lr_pca.5.50.5_n5_av<-c(0.7581, 0.0382, 0.0898)

lr_kde.5.50.5_n1_av<-c(0.8576, 0.9137, 0.9832)
lr_kde.5.50.5_n2_av<-c(0.8485, 0.8797, 0.9810)
lr_kde.5.50.5_n3_av<-c(0.8434, 0.8206, 0.9767)
lr_kde.5.50.5_n4_av<-c(0.8283, 0.6468, 0.9749)
lr_kde.5.50.5_n5_av<-c(0.8221, 0.5000, 0.9592)

   
########################   ROPE PCA

diff_lr_pca.5.50.5_n1_av<-lr_dataset_av - lr_pca.5.50.5_n1_av

bsr_diff_lr_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1461667
## 
## $winRight
## [1] 0.8538333
bsr_diff_lr_pca.5.50.5_n1_av_odds.left<-bsr_diff_lr_pca.5.50.5_n1_av$winLeft/bsr_diff_lr_pca.5.50.5_n1_av$winRight
bsr_diff_lr_pca.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n2_av<-lr_dataset_av - lr_pca.5.50.5_n2_av

bsr_diff_lr_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.2946667
## 
## $winRight
## [1] 0.7053333
bsr_diff_lr_pca.5.50.5_n2_av_odds.left<-bsr_diff_lr_pca.5.50.5_n2_av$winLeft/bsr_diff_lr_pca.5.50.5_n2_av$winRight
bsr_diff_lr_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n3_av<-lr_dataset_av - lr_pca.5.50.5_n3_av

bsr_diff_lr_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009166667
## 
## $winRight
## [1] 0.9908333
bsr_diff_lr_pca.5.50.5_n3_av_odds.left<-bsr_diff_lr_pca.5.50.5_n3_av$winLeft/bsr_diff_lr_pca.5.50.5_n3_av$winRight
bsr_diff_lr_pca.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n4_av<-lr_dataset_av - lr_pca.5.50.5_n4_av

bsr_diff_lr_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009133333
## 
## $winRight
## [1] 0.9908667
bsr_diff_lr_pca.5.50.5_n4_av_odds.left<-bsr_diff_lr_pca.5.50.5_n4_av$winLeft/bsr_diff_lr_pca.5.50.5_n4_av$winRight
bsr_diff_lr_pca.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n5_av<-lr_dataset_av - lr_pca.5.50.5_n5_av

bsr_diff_lr_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008166667
## 
## $winRight
## [1] 0.9918333
bsr_diff_lr_pca.5.50.5_n5_av_odds.left<-bsr_diff_lr_pca.5.50.5_n5_av$winLeft/bsr_diff_lr_pca.5.50.5_n5_av$winRight
bsr_diff_lr_pca.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_lr_kde.5.50.5_n1_av<-lr_dataset_av - lr_kde.5.50.5_n1_av

bsr_diff_lr_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 1
## 
## $winRight
## [1] 0
bsr_diff_lr_kde.5.50.5_n1_av_odds.left<-bsr_diff_lr_kde.5.50.5_n1_av$winLeft/bsr_diff_lr_kde.5.50.5_n1_av$winRight
bsr_diff_lr_kde.5.50.5_n1_av_odds.left
## [1] NaN
plot(rope(diff_lr_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n2_av<-lr_dataset_av - lr_kde.5.50.5_n2_av

bsr_diff_lr_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.5769
## 
## $winRight
## [1] 0.4231
bsr_diff_lr_kde.5.50.5_n2_av_odds.left<-bsr_diff_lr_kde.5.50.5_n2_av$winLeft/bsr_diff_lr_kde.5.50.5_n2_av$winRight
bsr_diff_lr_kde.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n3_av<-lr_dataset_av - lr_kde.5.50.5_n3_av

bsr_diff_lr_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.3884667
## 
## $winRight
## [1] 0.6115333
bsr_diff_lr_kde.5.50.5_n3_av_odds.left<-bsr_diff_lr_kde.5.50.5_n3_av$winLeft/bsr_diff_lr_kde.5.50.5_n3_av$winRight
bsr_diff_lr_kde.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n4_av<-lr_dataset_av - lr_kde.5.50.5_n4_av

bsr_diff_lr_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1435333
## 
## $winRight
## [1] 0.8564667
bsr_diff_lr_kde.5.50.5_n4_av_odds.left<-bsr_diff_lr_kde.5.50.5_n4_av$winLeft/bsr_diff_lr_kde.5.50.5_n4_av$winRight
bsr_diff_lr_kde.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n5_av<-lr_dataset_av - lr_kde.5.50.5_n5_av

bsr_diff_lr_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n5_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008633333
## 
## $winRight
## [1] 0.9913667
bsr_diff_lr_kde.5.50.5_n5_av_odds.left<-bsr_diff_lr_kde.5.50.5_n5_av$winLeft/bsr_diff_lr_kde.5.50.5_n5_av$winRight
bsr_diff_lr_kde.5.50.5_n5_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.50.5_n5_av,c(-0.01,0.01)))

####################################################   Naive Bayes

##Naive Bayes Results

nb_dataset_av<-c(0.7751, 0.8944, 0.9706)

nb_pca.5.50.5_n1_av<-c(0.2408, 0.6196, 0.9268)
#nb_pca.5.50.5_n2_av<-c(0.4466, NA, 0.9557)
#nb_pca.5.50.5_n3_av<-c(0.7600, NA, 0.9011)
nb_pca.5.50.5_n4_av<-c(0.7592, 0.3615, 0.7500)
#nb_pca.5.50.5_n5_av<-c(0.7592, NA, NA)

#nb_kde.5.50.5_n1_av<-c(0.7734, NA, 0.9750)
#nb_kde.5.50.5_n2_av<-c(0.58463283, NA, 0.9557)
#nb_kde.5.50.5_n3_av<-c(0.7600, NA, 0.9011)
nb_kde.5.50.5_n4_av<-c(0.7592, 0.3615, 0.7500)
#nb_kde.5.50.5_n5_av<-c(0.7592, NA, NA)



   
########################   ROPE PCA

diff_nb_pca.5.50.5_n1_av<-nb_dataset_av - nb_pca.5.50.5_n1_av

bsr_diff_nb_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nb_pca.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0093
## 
## $winRight
## [1] 0.9907
bsr_diff_nb_pca.5.50.5_n1_av_odds.left<-bsr_diff_nb_pca.5.50.5_n1_av$winLeft/bsr_diff_nb_pca.5.50.5_n1_av$winRight
bsr_diff_nb_pca.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nb_pca.5.50.5_n1_av,c(-0.01,0.01)))

#diff_nb_pca.5.50.5_n2_av<-nb_dataset_av - nb_pca.5.50.5_n2_av

#bsr_diff_nb_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n2_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n2_av

#bsr_diff_nb_pca.5.50.5_n2_av_odds.left<-bsr_diff_nb_pca.5.50.5_n2_av$winLeft/bsr_diff_nb_pca.5.50.5_n2_av$winRight
#bsr_diff_nb_pca.5.50.5_n2_av_odds.left

#plot(rope(diff_nb_pca.5.50.5_n2_av,c(-0.01,0.01)))


#diff_nb_pca.5.50.5_n3_av<-nb_dataset_av - nb_pca.5.50.5_n3_av

#bsr_diff_nb_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n3_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n3_av

#bsr_diff_nb_pca.5.50.5_n3_av_odds.left<-bsr_diff_nb_pca.5.50.5_n3_av$winLeft/bsr_diff_nb_pca.5.50.5_n3_av$winRight
#bsr_diff_nb_pca.5.50.5_n3_av_odds.left

#plot(rope(diff_nb_pca.5.50.5_n3_av,c(-0.01,0.01)))


diff_nb_pca.5.50.5_n4_av<-nb_dataset_av - nb_pca.5.50.5_n4_av

bsr_diff_nb_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nb_pca.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.03606667
## 
## $winRight
## [1] 0.9639333
bsr_diff_nb_pca.5.50.5_n4_av_odds.left<-bsr_diff_nb_pca.5.50.5_n4_av$winLeft/bsr_diff_nb_pca.5.50.5_n4_av$winRight
bsr_diff_nb_pca.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_nb_pca.5.50.5_n4_av,c(-0.01,0.01)))

#diff_nb_pca.5.50.5_n5_av<-nb_dataset_av - nb_pca.5.50.5_n5_av

#bsr_diff_nb_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n5_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n5_av

#bsr_diff_nb_pca.5.50.5_n5_av_odds.left<-bsr_diff_nb_pca.5.50.5_n5_av$winLeft/bsr_diff_nb_pca.5.50.5_n5_av $winRight
#bsr_diff_nb_pca.5.50.5_n5_av_odds.left

#plot(rope(diff_nb_pca.5.50.5_n5_av,c(-0.01,0.01)))


##########################    ROPE KDE

#diff_nb_kde.5.50.5_n1_av<-nb_dataset_av - nb_kde.5.50.5_n1_av

#bsr_diff_nb_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n1_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n1_av

#bsr_diff_nb_kde.5.50.5_n1_av_odds.left<-bsr_diff_nb_kde.5.50.5_n1_av$winLeft/bsr_diff_nb_kde.5.50.5_n1_av$winRight
#bsr_diff_nb_kde.5.50.5_n1_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n1_av,c(-0.01,0.01)))

#diff_nb_kde.5.50.5_n2_av<-nb_dataset_av - nb_kde.5.50.5_n2_av

#bsr_diff_nb_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n2_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n2_av

#bsr_diff_nb_kde.5.50.5_n2_av_odds.left<-bsr_diff_nb_kde.5.50.5_n2_av$winLeft/bsr_diff_nb_kde.5.50.5_n2_av$winRight
#bsr_diff_nb_kde.5.50.5_n2_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n2_av,c(-0.01,0.01)))


#diff_nb_kde.5.50.5_n3_av<-nb_dataset_av - nb_kde.5.50.5_n3_av

#bsr_diff_nb_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n3_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n3_av

#bsr_diff_nb_kde.5.50.5_n3_av_odds.left<-bsr_diff_nb_kde.5.50.5_n3_av $winLeft/bsr_diff_nb_kde.5.50.5_n3_av $winRight
#bsr_diff_nb_kde.5.50.5_n3_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n3_av,c(-0.01,0.01)))


diff_nb_kde.5.50.5_n4_av<-nb_dataset_av - nb_kde.5.50.5_n4_av

bsr_diff_nb_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nb_kde.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0361
## 
## $winRight
## [1] 0.9639
bsr_diff_nb_kde.5.50.5_n4_av_odds.left<-bsr_diff_nb_kde.5.50.5_n4_av $winLeft/bsr_diff_nb_kde.5.50.5_n4_av $winRight
bsr_diff_nb_kde.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_nb_kde.5.50.5_n4_av,c(-0.01,0.01)))

#diff_nb_kde.5.50.5_n5_av<-nb_dataset_av - nb_kde.5.50.5_n5_av

#bsr_diff_nb_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n5_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n5_av

#bsr_diff_nb_kde.5.50.5_n5_av_odds.left<-bsr_diff_nb_kde.5.50.5_n5_av $winLeft/bsr_diff_nb_kde.5.50.5_n5_av $winRight
#bsr_diff_nb_kde.5.50.5_n5_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n5_av,c(-0.01,0.01)))